You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
23 May 2013Zero curvature particle flow for nonlinear filters
We derive a new algorithm for computing Bayes’
rule using particle flow that has zero curvature. The flow is
computed by solving a vector Riccati equation exactly in
closed form rather than solving a PDE, with a significant
reduction in computational complexity. Our theory is valid
for any smooth nowhere vanishing probability densities,
including highly multimodal non-Gaussian densities. We
show that this new flow is similar to the extended Kalman
filter in the special case of nonlinear measurements with
Gaussian noise. We also outline more general particle flows,
including: constant curvature, geodesic flow, non-constant
curvature, piece-wise constant curvature, etc.
Fred Daum andJim Huang
"Zero curvature particle flow for nonlinear filters", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87450Q (23 May 2013); https://doi.org/10.1117/12.2009364
The alert did not successfully save. Please try again later.
Fred Daum, Jim Huang, "Zero curvature particle flow for nonlinear filters," Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87450Q (23 May 2013); https://doi.org/10.1117/12.2009364